DeepOPF: Deep Neural Network for DC Optimal Power Flow
DeepOPF: Deep Neural Network for DC Optimal Power Flow
复制标题
DOI：
10.1109/smartgridcomm.2019.8909795
复制
发表时间：
2019-05
期刊：
影响因子：
--
通讯作者：
Xiang Pan;Tianyu Zhao;Minghua Chen
中科院分区：
文献类型：
--
作者：
Xiang Pan;Tianyu Zhao;Minghua Chen
我们将DeepOPF开发为一种基于深度神经网络（DNN）的方法，用于解决直流最优潮流（DC - OPF）问题。DeepOPF的灵感来源于这样一种观察：对于给定的电网求解DC - OPF等同于描述负荷输入与调度和输电决策之间的高维映射。我们构建并训练一个DNN模型来学习这种映射，然后将其应用于在任意负荷输入下获得优化的运行决策。我们采用均匀采样来解决通用DNN方法中常见的过拟合问题。我们利用DC - OPF中的一种有用结构来显著降低映射维度，进而减小我们的DNN模型的规模以及所需的训练数据量/时间。我们还设计了一个后处理程序以确保所获得解的可行性。IEEE测试案例的仿真结果表明，DeepOPF总是能生成具有可忽略最优性损失的可行解，同时与在先进求解器中实现的传统方法相比，将计算时间加快了两个数量级。
We develop DeepOPF as a Deep Neural Network (DNN) based approach for solving direct current optimal power flow (DC-OPF) problems. DeepOPF is inspired by the observation that solving DC-OPF for a given power network is equivalent to characterizing a high-dimensional mapping between the load inputs and the dispatch and transmission decisions. We construct and train a DNN model to learn such mapping, then we apply it to obtain optimized operating decisions upon arbitrary load inputs. We adopt uniform sampling to address the over-fitting problem common in generic DNN approaches. We leverage on a useful structure in DC-OPF to significantly reduce the mapping dimension, subsequently cutting down the size of our DNN model and the amount of training data/time needed. We also design a post-processing procedure to ensure the feasibility of the obtained solution. Simulation results of IEEE test cases show that DeepOPF always generates feasible solutions with negligible optimality loss, while speeding up the computing time by two orders of magnitude as compared to conventional approaches implemented in a state-of-the-art solver.